A Simple State-Contingent Pricing Rule for Complex Intertemporal Externalities

Some externalities, such as global warming, involve complex and uncertain relationships between emissions and the environment, with effects acting over lags the lengths of which are themselves subject to uncertainty. Optimal pricing rules can be derived in principle, but application is often controversial due to the necessity of assuming key model parameters which are imprecisely known. Bayesian learning methods may help reduce uncertainty, but only with time lags that preclude immediate policy guidance. This paper argues that the situation giving rise to a complex intertemporal externality also yields an observable state variable that can, under a simple transformation, yield an approximation to the optimal externality price. I outline the assumptions necessary to derive the transformation, and present numerical examples that illustrate its ability to follow linear and nonlinear first-best price paths. A specific application to greenhouse gases is proposed